First of all, I want to point out that it is the first time I am using dynamic polymorphism and the composite design pattern.
I would like to use the composite design pattern to create a class Tree which is able to take different objects of the type Tree, a composite type, or Leaf, an atomic type. Both Tree and Leaf inherit from a common class Nature. Tree can store Leaf or Tree objects into a std::vector<std::shared_ptr<Nature>> children. I would like to fill the vector children with a syntax of this kind (so I guess I have to use variadic, to consider a generic number of inputs in the input lists), as in the following:
Leaf l0(0);
Leaf l1(1);
Tree t0;
Tree t1;
t0.add(l0,l1);
t1.add(t0,l0,l1); // or in general t1.add(t_00,...,t_0n, l_00,...,l_0n,t10,...,t1n,l10,...,l1n,.... )
Then I would also access different elements of a Tree by means of the operator[ ]. So for example t1[0] returns t0 and t1[0][0] returns l0, while t1[0][1] returns l0.
Also I would like an homogeneous behaviour. So either use -> or the dot for accessing the methods on all levels (tree or leaf).
Is it possible to achieve this behaviour?
The implementation of such classes can be like the following:
class Nature
{
public:
virtual void nature_method() = 0;
virtual~Nature();
//virtual Nature& operator[] (int x);
};
class Leaf: public Nature
{
int value;
public:
Leaf(int val)
{
value = val;
}
void nature_method() override
{
std::cout << " Leaf=="<<value<<" ";
}
};
class Tree: public Nature
{
private:
std::vector <std::shared_ptr< Nature > > children;
int value;
public:
Tree(int val)
{
value = val;
}
void add(const Nature&);
void add(const Leaf& c)
{
children.push_back(std::make_shared<Leaf>(c));
}
void add(const Tree& c)
{
children.push_back(std::make_shared<Tree>(c));
}
void add(std::shared_ptr<Nature> c)
{
children.push_back(c);
}
template<typename...Args>
typename std::enable_if<0==sizeof...(Args), void>::type
add(const Leaf& t,Args...more)
{
children.push_back(std::make_shared<Leaf>(t));
};
template<typename...Args>
typename std::enable_if<0==sizeof...(Args), void>::type
add(const Tree& t,Args...more)
{
children.push_back(std::make_shared<Tree>(t));
};
template<typename...Args>
typename std::enable_if<0<sizeof...(Args), void>::type
add(const Leaf& t,Args...more)
{
children.push_back(std::make_shared<Leaf>(t));
add(more...);
};
template<typename...Args>
typename std::enable_if<0<sizeof...(Args), void>::type
add(const Tree& t,Args...more)
{
children.push_back(std::make_shared<Tree>(t));
add(more...);
};
void nature_method() override
{
std::cout << " Tree=="<< value;
for (int i = 0; i < children.size(); i++)
children[i]->nature_method();
}
}
I could implement the overload operator [] to return a pointer to Nature or a Nature object, like so:
Nature& operator[] (int x) {
return *children[x];
}
std::shared_ptr< Nature > operator[] (int x) {
return children[x];
}
In both cases, the return type is Nature related. This because it could be a Leaf or a Tree, which is not known in advance. But since the return type of the operator has to be known at compile time, I cannot do something else.
However, if the returned type would be Tree related, I cannot use the operator [] anymore, because I have enforced it to be Nature.
How can I dynamically choose the return type, Tree or Leaf related, of []? Is there any workaround for this?
I could consider operator [] a virtual method in the Nature class, but still I would no what to make out of this.
I have read about covariant types as well, but I do not know if they would be applicable here.
Thank you.
If you want to be type-safe, the return value of [] will have to be checked at each use site to determine if it is a Tree or a Leaf.
You could also choose not to be type-safe, and invoke undefined behaviour if you use a Leaf in a way that is supposed to be a Tree.
Regardless:
virtual Nature& operator[](std::ptrdiff_t i) {
throw std::invalid_argument("Not a Tree");
}
virtual Nature const& operator[](std::ptrdiff_t i) const {
throw std::invalid_argument("Not a Tree");
}
in Nature, followed by:
virtual Nature& operator[](std::ptrdiff_t i) final override {
auto r = children.at((std::size_t)x);
if (r) return *r;
throw std::out_of_range("no element there");
}
virtual Nature const& operator[](std::ptrdiff_t i) const final override {
auto r = children.at((std::size_t)x);
if (r) return *r;
throw std::out_of_range("no element there");
}
in Tree.
That'll spawn exceptions when you use [] on the wrong type.
I have a use case where one thread reads message into a large buffer and the distributes the processing to a bunch of threads. The buffer is shared by multiple threads after that. Its read-only and when the last thread finishes, the buffer has to be freed. The buffer is allocated from a lock-free slab allocator.
My initial design was to use shared_ptr for the buffer. But the buffer can be of different size. My way of getting around it was do something like this.
struct SharedBuffer {
SharedBuffer (uint16_t len, std::shared_ptr<void> ptr)
: _length(len), _buf(std::move(ptr))
{
}
uint8_t data () { return (uint8_t *)_buf.get(); }
uint16_t length
std::shared_ptr<void> _buf; // type-erase the shared_ptr as the SharedBuffer
// need to stored in some other structs
};
Now the allocator will allocate the shared_ptr like this:
SharedBuffer allocate (size_t size)
{
auto buf = std::allocate_shared<std::array<uint8_t, 16_K>>(myallocator);
return SharedBuffer{16_K, buf}; // type erase the std::array
}
And the SharedBuffer is enqueued to each thread who wants it.
Now I think, I am doing lot of stuff unnecessarily, I can sort of make do with boost::intrusive_ptr with the below scheme. Things are bit C'ish- as I am using variable size array. Here I have changed the slab allocator with a operator new() for the sake of simplicity. I wanted to run it by to see if this implementation is okay.
template <typename T>
inline int atomicIncrement (T* t)
{
return __atomic_add_fetch(&t->_ref, 1, __ATOMIC_ACQUIRE);
}
template <typename T>
inline int atomicDecrement (T* t)
{
return __atomic_sub_fetch(&t->_ref, 1, __ATOMIC_RELEASE);
}
class SharedBuffer {
public:
friend int atomicIncrement<SharedBuffer>(SharedBuffer*);
friend int atomicDecrement<SharedBuffer>(SharedBuffer*);
SharedBuffer(uint16_t len) : _length(len) {}
uint8_t *data ()
{
return &_data[0];
}
uint16_t length () const
{
return _length;
}
private:
int _ref{0};
const uint16_t _length;
uint8_t _data[];
};
using SharedBufferPtr = boost::intrusive_ptr<SharedBuffer>;
SharedBufferPtr allocate (size_t size)
{
// dummy implementation
void *p = ::operator new (size + sizeof(SharedBuffer));
// I am not explicitly constructing the array of uint8_t
return new (p) SharedBuffer(size);
}
void deallocate (SharedBuffer* sbuf)
{
sbuf->~SharedBuffer();
// dummy implementation
::operator delete ((void *)sbuf);
}
void intrusive_ptr_add_ref(SharedBuffer* sbuf)
{
atomicIncrement(sbuf);
}
void intrusive_ptr_release (SharedBuffer* sbuf)
{
if (atomicDecrement(sbuf) == 0) {
deallocate(sbuf);
}
}
I'd use the simpler implementation (using shared_ptr) unless you are avoiding specific problems (i.e. profile first).
Side Note: you can use boost::shared_pointer<> with boost::make_shared<T[]>(N), which is being [added to the standard library in c++20.
Note that allocate_shared already embeds the control block into the same allocation like you do with the intrusive approach.
Finally, I'd use std::atomic_int so you have a clear contract that cannot (accidentally) be used wrong. At the same time, it'll remove the remaining bit of complexity.
A 2-way merge is widely studied as a part of Mergesort algorithm.
But I am interested to find out the best way one can perform an N-way merge?
Lets say, I have N files which have sorted 1 million integers each.
I have to merge them into 1 single file which will have those 100 million sorted integers.
Please keep in mind that use case for this problem is actually external sorting which is disk based. Therefore, in real scenarios there would be memory limitation as well. So a naive approach of merging 2 files at a time (99 times) won't work. Lets say we have only a small sliding window of memory available for each array.
I am not sure if there is already a standardized solution to this N-way merge. (Googling didn't tell me much).
But if you know if a good n-way merge algorithm, please post algo/link.
Time complexity: If we greatly increase the number of files (N) to be merged, how would that affect the time complexity of your algorithm?
Thanks for your answers.
I haven't been asked this anywhere, but I felt this could be an interesting interview question. Therefore tagged.
How about the following idea:
Create a priority queue
Iterate through each file f
enqueue the pair (nextNumberIn(f), f) using the first value as priority key
While queue not empty
dequeue head (m, f) of queue
output m
if f not depleted
enqueue (nextNumberIn(f), f)
Since adding elements to a priority queue can be done in logarithmic time, item 2 is O(N × log N). Since (almost all) iterations of the while loop adds an element, the whole while-loop is O(M × log N) where M is the total number of numbers to sort.
Assuming all files have a non-empty sequence of numbers, we have M > N and thus the whole algorithm should be O(M × log N).
Search for "Polyphase merge", check out classics - Donald Knuth & E.H.Friend.
Also, you may want to take a look at the proposed Smart Block Merging by Seyedafsari & Hasanzadeh, that, similarly to earlier suggestions, uses priority queues.
Another interesting reasonsing is In Place Merging Algorithm by Kim & Kutzner.
I also recommend this paper by Vitter: External memory algorithms and data structures: dealing with massive data.
One simple idea is to keep a priority queue of the ranges to merge, stored in such a way that the range with the smallest first element is removed first from the queue. You can then do an N-way merge as follows:
Insert all of the ranges into the priority queue, excluding empty ranges.
While the priority queue is not empty:
Dequeue the smallest element from the queue.
Append the first element of this range to the output sequence.
If it's nonempty, insert the rest of the sequence back into the priority queue.
The correctness of this algorithm is essentially a generalization of the proof that a 2-way merge works correctly - if you always add the smallest element from any range, and all the ranges are sorted, you end up with the sequence as a whole sorted.
The runtime complexity of this algorithm can be found as follows. Let M be the total number of elements in all the sequences. If we use a binary heap, then we do at most O(M) insertions and O(M) deletions from the priority queue, since for each element written to the output sequence there's a dequeue to pull out the smallest sequence, followed by an enqueue to put the rest of the sequence back into the queue. Each of these steps takes O(lg N) operations, because insertion or deletion from a binary heap with N elements in it takes O(lg N) time. This gives a net runtime of O(M lg N), which grows less than linearly with the number of input sequences.
There may be a way to get this even faster, but this seems like a pretty good solution. The memory usage is O(N) because we need O(N) overhead for the binary heap. If we implement the binary heap by storing pointers to the sequences rather than the sequences themselves, this shouldn't be too much of a problem unless you have a truly ridiculous number of sequences to merge. In that case, just merge them in groups that do fit into memory, then merge all the results.
Hope this helps!
A simple approach to Merging k sorted arrays (each of length n) requires O(n k^2) time and not O(nk) time. As when you merge first 2 arrays it takes 2n time, then when you merge third with the output , it takes 3n time as now we are merging two array of length 2n and n. Now when we merge this output with the fourth one,this merge requires 4n time.Thus the last merge (when we are adding the kth array to our already sorted array ) requires k*n time.Thus total time required is 2n+ 3n + 4n +...k*n which is O(n k^2).
It looks like we can do it in O(kn) time but it is not so because each time our array which we are merging is increasing in size.
Though we can achieve a better bound using divide and conquer. I am still working on that and post a solution if I find one.
See http://en.wikipedia.org/wiki/External_sorting. Here is my take on the heap based k-way merge, using a buffered read from the sources to emulate I/O reduction:
public class KWayMerger<T>
{
private readonly IList<T[]> _sources;
private readonly int _bufferSize;
private readonly MinHeap<MergeValue<T>> _mergeHeap;
private readonly int[] _indices;
public KWayMerger(IList<T[]> sources, int bufferSize, Comparer<T> comparer = null)
{
if (sources == null) throw new ArgumentNullException("sources");
_sources = sources;
_bufferSize = bufferSize;
_mergeHeap = new MinHeap<MergeValue<T>>(
new MergeComparer<T>(comparer ?? Comparer<T>.Default));
_indices = new int[sources.Count];
}
public T[] Merge()
{
for (int i = 0; i <= _sources.Count - 1; i++)
AddToMergeHeap(i);
var merged = new T[_sources.Sum(s => s.Length)];
int mergeIndex = 0;
while (_mergeHeap.Count > 0)
{
var min = _mergeHeap.ExtractDominating();
merged[mergeIndex++] = min.Value;
if (min.Source != -1) //the last item of the source was extracted
AddToMergeHeap(min.Source);
}
return merged;
}
private void AddToMergeHeap(int sourceIndex)
{
var source = _sources[sourceIndex];
var start = _indices[sourceIndex];
var end = Math.Min(start + _bufferSize - 1, source.Length - 1);
if (start > source.Length - 1)
return; //we're done with this source
for (int i = start; i <= end - 1; i++)
_mergeHeap.Add(new MergeValue<T>(-1, source[i]));
//only the last item should trigger the next buffered read
_mergeHeap.Add(new MergeValue<T>(sourceIndex, source[end]));
_indices[sourceIndex] += _bufferSize; //we may have added less items,
//but if we did we've reached the end of the source so it doesn't matter
}
}
internal class MergeValue<T>
{
public int Source { get; private set; }
public T Value { get; private set; }
public MergeValue(int source, T value)
{
Value = value;
Source = source;
}
}
internal class MergeComparer<T> : IComparer<MergeValue<T>>
{
public Comparer<T> Comparer { get; private set; }
public MergeComparer(Comparer<T> comparer)
{
if (comparer == null) throw new ArgumentNullException("comparer");
Comparer = comparer;
}
public int Compare(MergeValue<T> x, MergeValue<T> y)
{
Debug.Assert(x != null && y != null);
return Comparer.Compare(x.Value, y.Value);
}
}
Here is one possible implementation of MinHeap<T>. Some tests:
[TestMethod]
public void TestKWaySort()
{
var rand = new Random();
for (int i = 0; i < 10; i++)
AssertKwayMerge(rand);
}
private static void AssertKwayMerge(Random rand)
{
var sources = new[]
{
GenerateRandomCollection(rand, 10, 30, 0, 30).OrderBy(i => i).ToArray(),
GenerateRandomCollection(rand, 10, 30, 0, 30).OrderBy(i => i).ToArray(),
GenerateRandomCollection(rand, 10, 30, 0, 30).OrderBy(i => i).ToArray(),
GenerateRandomCollection(rand, 10, 30, 0, 30).OrderBy(i => i).ToArray(),
};
Assert.IsTrue(new KWayMerger<int>(sources, 20).Merge().SequenceEqual(sources.SelectMany(s => s).OrderBy(i => i)));
}
public static IEnumerable<int> GenerateRandomCollection(Random rand, int minLength, int maxLength, int min = 0, int max = int.MaxValue)
{
return Enumerable.Repeat(0, rand.Next(minLength, maxLength)).Select(i => rand.Next(min, max));
}
I wrote this STL-style piece of code that does N-way merge and thought I'd post it here to help prevent others from reinventing the wheel. :)
Warning: it's only mildly tested. Test before use. :)
You can use it like this:
#include <vector>
int main()
{
std::vector<std::vector<int> > v;
std::vector<std::vector<int>::iterator> vout;
std::vector<int> v1;
std::vector<int> v2;
v1.push_back(1);
v1.push_back(2);
v1.push_back(3);
v2.push_back(0);
v2.push_back(1);
v2.push_back(2);
v.push_back(v1);
v.push_back(v2);
multiway_merge(v.begin(), v.end(), std::back_inserter(vout), false);
}
It also allows using pairs of iterators instead of the containers themselves.
If you use Boost.Range, you can remove some of the boilerplate code.
The code:
#include <algorithm>
#include <functional> // std::less
#include <iterator>
#include <queue> // std::priority_queue
#include <utility> // std::pair
#include <vector>
template<class OutIt>
struct multiway_merge_value_insert_iterator : public std::iterator<
std::output_iterator_tag, OutIt, ptrdiff_t
>
{
OutIt it;
multiway_merge_value_insert_iterator(OutIt const it = OutIt())
: it(it) { }
multiway_merge_value_insert_iterator &operator++(int)
{ return *this; }
multiway_merge_value_insert_iterator &operator++()
{ return *this; }
multiway_merge_value_insert_iterator &operator *()
{ return *this; }
template<class It>
multiway_merge_value_insert_iterator &operator =(It const i)
{
*this->it = *i;
++this->it;
return *this;
}
};
template<class OutIt>
multiway_merge_value_insert_iterator<OutIt>
multiway_merge_value_inserter(OutIt const it)
{ return multiway_merge_value_insert_iterator<OutIt>(it); };
template<class Less>
struct multiway_merge_value_less : private Less
{
multiway_merge_value_less(Less const &less) : Less(less) { }
template<class It1, class It2>
bool operator()(
std::pair<It1, It1> const &b /* inverted */,
std::pair<It2, It2> const &a) const
{
return b.first != b.second && (
a.first == a.second ||
this->Less::operator()(*a.first, *b.first));
}
};
struct multiway_merge_default_less
{
template<class T>
bool operator()(T const &a, T const &b) const
{ return std::less<T>()(a, b); }
};
template<class R>
struct multiway_merge_range_iterator
{ typedef typename R::iterator type; };
template<class R>
struct multiway_merge_range_iterator<R const>
{ typedef typename R::const_iterator type; };
template<class It>
struct multiway_merge_range_iterator<std::pair<It, It> >
{ typedef It type; };
template<class R>
typename R::iterator multiway_merge_range_begin(R &r)
{ return r.begin(); }
template<class R>
typename R::iterator multiway_merge_range_end(R &r)
{ return r.end(); }
template<class R>
typename R::const_iterator multiway_merge_range_begin(R const &r)
{ return r.begin(); }
template<class R>
typename R::const_iterator multiway_merge_range_end(R const &r)
{ return r.end(); }
template<class It>
It multiway_merge_range_begin(std::pair<It, It> const &r)
{ return r.first; }
template<class It>
It multiway_merge_range_end(std::pair<It, It> const &r)
{ return r.second; }
template<class It, class OutIt, class Less, class PQ>
OutIt multiway_merge(
It begin, It const end, OutIt out, Less const &less,
PQ &pq, bool const distinct = false)
{
while (begin != end)
{
pq.push(typename PQ::value_type(
multiway_merge_range_begin(*begin),
multiway_merge_range_end(*begin)));
++begin;
}
while (!pq.empty())
{
typename PQ::value_type top = pq.top();
pq.pop();
if (top.first != top.second)
{
while (!pq.empty() && pq.top().first == pq.top().second)
{ pq.pop(); }
if (!distinct ||
pq.empty() ||
less(*pq.top().first, *top.first) ||
less(*top.first, *pq.top().first))
{
*out = top.first;
++out;
}
++top.first;
pq.push(top);
}
}
return out;
}
template<class It, class OutIt, class Less>
OutIt multiway_merge(
It const begin, It const end, OutIt out, Less const &less,
bool const distinct = false)
{
typedef typename multiway_merge_range_iterator<
typename std::iterator_traits<It>::value_type
>::type SubIt;
if (std::distance(begin, end) < 16)
{
typedef std::vector<std::pair<SubIt, SubIt> > Remaining;
Remaining remaining;
remaining.reserve(
static_cast<size_t>(std::distance(begin, end)));
for (It i = begin; i != end; ++i)
{
if (multiway_merge_range_begin(*i) !=
multiway_merge_range_end(*i))
{
remaining.push_back(std::make_pair(
multiway_merge_range_begin(*i),
multiway_merge_range_end(*i)));
}
}
while (!remaining.empty())
{
typename Remaining::iterator smallest =
remaining.begin();
for (typename Remaining::iterator
i = remaining.begin();
i != remaining.end();
)
{
if (less(*i->first, *smallest->first))
{
smallest = i;
++i;
}
else if (distinct && i != smallest &&
!less(
*smallest->first,
*i->first))
{
i = remaining.erase(i);
}
else { ++i; }
}
*out = smallest->first;
++out;
++smallest->first;
if (smallest->first == smallest->second)
{ smallest = remaining.erase(smallest); }
}
return out;
}
else
{
std::priority_queue<
std::pair<SubIt, SubIt>,
std::vector<std::pair<SubIt, SubIt> >,
multiway_merge_value_less<Less>
> q((multiway_merge_value_less<Less>(less)));
return multiway_merge(begin, end, out, less, q, distinct);
}
}
template<class It, class OutIt>
OutIt multiway_merge(
It const begin, It const end, OutIt const out,
bool const distinct = false)
{
return multiway_merge(
begin, end, out,
multiway_merge_default_less(), distinct);
}
Here is my implementation using MinHeap...
package merging;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
public class N_Way_Merge {
int No_of_files=0;
String[] listString;
int[] listIndex;
PrintWriter pw;
private String fileDir = "D:\\XMLParsing_Files\\Extracted_Data";
private File[] fileList;
private BufferedReader[] readers;
public static void main(String[] args) throws IOException {
N_Way_Merge nwm=new N_Way_Merge();
long start= System.currentTimeMillis();
try {
nwm.createFileList();
nwm.createReaders();
nwm.createMinHeap();
}
finally {
nwm.pw.flush();
nwm.pw.close();
for (BufferedReader readers : nwm.readers) {
readers.close();
}
}
long end = System.currentTimeMillis();
System.out.println("Files merged into a single file.\nTime taken: "+((end-start)/1000)+"secs");
}
public void createFileList() throws IOException {
//creates a list of sorted files present in a particular directory
File folder = new File(fileDir);
fileList = folder.listFiles();
No_of_files=fileList.length;
assign();
System.out.println("No. of files - "+ No_of_files);
}
public void assign() throws IOException
{
listString = new String[No_of_files];
listIndex = new int[No_of_files];
pw = new PrintWriter(new BufferedWriter(new FileWriter("D:\\XMLParsing_Files\\Final.txt", true)));
}
public void createReaders() throws IOException {
//creates array of BufferedReaders to read the files
readers = new BufferedReader[No_of_files];
for(int i=0;i<No_of_files;++i)
{
readers[i]=new BufferedReader(new FileReader(fileList[i]));
}
}
public void createMinHeap() throws IOException {
for(int i=0;i<No_of_files;i++)
{
listString[i]=readers[i].readLine();
listIndex[i]=i;
}
WriteToFile(listString,listIndex);
}
public void WriteToFile(String[] listString,int[] listIndex) throws IOException{
BuildHeap_forFirstTime(listString, listIndex);
while(!(listString[0].equals("zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz")))
{
pw.println(listString[0]);
listString[0]=readers[listIndex[0]].readLine();
MinHeapify(listString,listIndex,0);
}
}
public void BuildHeap_forFirstTime(String[] listString,int[] listIndex){
for(int i=(No_of_files/2)-1;i>=0;--i)
MinHeapify(listString,listIndex,i);
}
public void MinHeapify(String[] listString,int[] listIndex,int index){
int left=index*2 + 1;
int right=left + 1;
int smallest=index;
int HeapSize=No_of_files;
if(left <= HeapSize-1 && listString[left]!=null && (listString[left].compareTo(listString[index])) < 0)
smallest = left;
if(right <= HeapSize-1 && listString[right]!=null && (listString[right].compareTo(listString[smallest])) < 0)
smallest=right;
if(smallest!=index)
{
String temp=listString[index];
listString[index]=listString[smallest];
listString[smallest]=temp;
listIndex[smallest]^=listIndex[index];
listIndex[index]^=listIndex[smallest];
listIndex[smallest]^=listIndex[index];
MinHeapify(listString,listIndex,smallest);
}
}
}
Java implementation of min heap algorithm for merging k sorted arrays:
public class MergeKSorted {
/**
* helper object to store min value of each array in a priority queue,
* the kth array and the index into kth array
*
*/
static class PQNode implements Comparable<PQNode>{
int value;
int kth = 0;
int indexKth = 0;
public PQNode(int value, int kth, int indexKth) {
this.value = value;
this.kth = kth;
this.indexKth = indexKth;
}
#Override
public int compareTo(PQNode o) {
if(o != null) {
return Integer.valueOf(value).compareTo(Integer.valueOf(o.value));
}
else return 0;
}
#Override
public String toString() {
return value+" "+kth+" "+indexKth;
}
}
public static void mergeKSorted(int[][] sortedArrays) {
int k = sortedArrays.length;
int resultCtr = 0;
int totalSize = 0;
PriorityQueue<PQNode> pq = new PriorityQueue<>();
for(int i=0; i<k; i++) {
int[] kthArray = sortedArrays[i];
totalSize+=kthArray.length;
if(kthArray.length > 0) {
PQNode temp = new PQNode(kthArray[0], i, 0);
pq.add(temp);
}
}
int[] result = new int[totalSize];
while(!pq.isEmpty()) {
PQNode temp = pq.poll();
int[] kthArray = sortedArrays[temp.kth];
result[resultCtr] = temp.value;
resultCtr++;
temp.indexKth++;
if(temp.indexKth < kthArray.length) {
temp = new PQNode(kthArray[temp.indexKth], temp.kth, temp.indexKth);
pq.add(temp);
}
}
print(result);
}
public static void print(int[] a) {
StringBuilder sb = new StringBuilder();
for(int v : a) {
sb.append(v).append(" ");
}
System.out.println(sb);
}
public static void main(String[] args) {
int[][] sortedA = {
{3,4,6,9},
{4,6,8,9,12},
{3,4,9},
{1,4,9}
};
mergeKSorted(sortedA);
}
}